Apparatus and method for image recognition

ABSTRACT

An object recognizing apparatus is provided which is capable of precisely recognizing an object in an input image with the use of a corresponding learning image even when a local-segment of the input image coincides with a learning-local-segment of another similar learning image. The apparatus comprises (1) image dividing means for dividing an input image, which is received from image input means, into local-segments, (2) similar-local-segment extracting means for extracting a similar learning-local-segment to the local-segment of the input image from a learning image database, (3) object position estimating means for estimating the position of an object to be identified in the input image from the coordinates of the local-segment and the coordinates of the learning-local-segment corresponding to the local-segment, (4) counting means for counting the local-segments coincide with their corresponding learning-local-segments, and (5) object determining means for judging that the object to be identified is present when a result of counting is greater than a predetermined number. Consequently, the object and its position in any input image can be detected at higher accuracy.

FIELD OF THE INVENTION

[0001] The present invention relates to an apparatus and a method forrecognizing an object displayed in an input image and releasing data ofits position and shape.

BACKGROUND OF THE INVENTION

[0002] One of conventional image recognizing apparatus is known asdisclosed in the Japanese Patent of (Publication No. 921610).

[0003]FIG. 35 is a block diagram of a conventional image recognizingapparatus which comprises:

[0004] (a) image input unit 3511 for receiving an image of interest;

[0005] (b) model memory unit 3512 which stores local models of an objectto be identified;

[0006] (c) matching process unit 3513 for matching each image segment ofthe input image with the local models;

[0007] (d) local data integrating unit 3514 for integrating anddisplaying, in probabilistic way, the position of the object to beidentified in a parameter space together with the position of the imagesegment depending on the degree of the matching of each image segment ofthe input image with its local model; and

[0008] (e) object position determining unit 3515 for determining imagesegments with the highest probability from the parameter space todetermine the position of the object to be identified in the inputimage.

[0009] The conventional image recognizing apparatus may carry out therecognizing operation with much difficulty as a number of similar localmodels of different models are increased.

[0010] Another conventional image recognizing apparatus is also known asdisclosed in the Japanese Patent (Publication No. 6-215140).

[0011]FIG. 36 is a block diagram of the another conventional imagerecognizing apparatus which comprises:

[0012] (a) display 3601 for displaying an image;

[0013] (b) main controller 3602 for controlling operations of the entiresystem;

[0014] (c) internal memory 3603 for storing an operating program and thelike;

[0015] (d) disk 3604 for storing a reference pattern;

[0016] (e) television camera 3605 for capturing an image of an object tobe identified such as a product or a sample;

[0017] (f) image input unit 3606 for converting image data of the objectcaptured by camera 3605 into a digital form;

[0018] (g) image rotating unit 3607 for positioning the object in agradation image of the digital form to be faced in a given direction foreach category;

[0019] (h) image data extracting unit 3608 for sampling the rotatedimage at a specific rate and extracting the gradation of each sampledimage as characteristic data of the rotated image;

[0020] (i) dictionary generating unit 3609, having average vectorcalculator 3609A for calculating an average vector of the images of eachcategory from the characteristic data, for determining a dictionary (alist of reference patterns) of the average vectors;

[0021] (j) identifying unit 3610 having vector distance comparator 3610Afor calculating a vector of an object of an unknown category and forextracting, from the dictionary generating unit 3609, one of the averagevectors which is closest to the calculated vector to identify the objecton the unknown category; and

[0022] (j) parameter setting unit 3611 for optimizing the parameters forimage input 3606, the image rotating unit 3607, image data extractingunit 3608, and identifying unit 3610 in each category.

[0023] The another conventional image recognizing apparatus may hardlycarry out the recognizing operation in case that the images of objectswhich are identical in the shape but different in the gradation aregrouped in one category for recognizing and classifying the objects byshape. Since similar gradation images are grouped into one category, atotal number of categories increases thus requiring more time for theoperation.

SUMMARY OF THE INVENTION

[0024] A first object of the present invention is to estimate theposition and the type of an object to be identified in an input image athigh accuracy even when local models of different types are verysimilar.

[0025] An image recognizing apparatus according to the present inventioncomprises:

[0026] (a) image input means for inputting an image;

[0027] (b) image dividing means for dividing the image received from theimage input means into input local-segments;

[0028] (c) similar window extracting means for extracting alearning-local-segment which is similar to each input local-segmentreceived from the image dividing means;

[0029] (d) object position estimating means for estimating a position ofan object to be identified in the input image from the coordinates ofthe input window and the coordinates of the learning window receivedfrom the similar window extracting means; and

[0030] (e) counting means for counting a pair of the learning window andthe input window for each position which is estimated from the learningwindow and the input window by the object position estimating means.

[0031] The operation of the image recognizing apparatus having the abovearrangement of the present invention includes:

[0032] (1) extracting a learning window which is similar to a inputwindow in the input image;

[0033] (2) estimating, in the input image, a position of a model in alearning image from the coordinates of the learning window in thelearning image and the coordinates of the corresponding input window inthe input image; and

[0034] (3) counting a pair of the learning window and the input windowfor each position estimated from the learning window and the inputwindow. Consequently, when the counted number is greater than apredetermined number, it is judged that the object of a type expressedwith the learning image is present in the input image, and the positionof the object can be estimated at high accuracy.

[0035] A second object of the present invention is to quickly recognizea shape of an object in an image and determine its position while theimages of objects which are identical in the shape but different in thegradation are grouped into one category.

[0036] Another image recognizing apparatus according to the presentinvention comprises:

[0037] (a) an image database for preliminarily storing a shapeidentifier specifying a shape of an object to be identified and imagesof the object having the shape;

[0038] (b) model generating means for preliminarily extractingcharacteristic data of the shape from the model images;

[0039] (c) a shape database for preliminarily storing the characteristicdata of the shape with its shape identifier;

[0040] (d) an image input unit for inputting an input image to beexamined;

[0041] (e) an image cutout unit for cutting out an image segment fromthe input image as a partial image;

[0042] (f) shape classifying means for determining whether or not theobject of the shape is present in the image segment by comparing theimage segment with the characteristic data of the shape; and

[0043] (g) an output unit for releasing, if there is an object having ashape which coincides with a shape in the input image, data about theshape of the object determined by the shape classifying means and aboutthe position of the shape of the object in the input image.

[0044] The another image recognizing apparatus according to the presentinvention allows the characteristic data of the shape to bepreliminarily extracted from many model images and to be compared withthe input image. Accordingly, the another image recognizing apparatuscan quickly examine whether or not the object is present in the inputimage from less amounts of data and, when so, readily provide theposition and the shape of the object.

BRIEF DESCRIPTION OF THE DRAWINGS

[0045]FIG. 1 is a block diagram of an image recognizing apparatusaccording to Embodiment 1 of the present invention;

[0046]FIG. 2 is a block diagram of the image recognizing apparatus ofEmbodiment 1 implemented by a computer;

[0047]FIG. 3 is a flowchart showing a procedure in Embodiment 1;

[0048]FIG. 4 illustrates an example of input image in Embodiment 1;

[0049]FIG. 5 illustrates examples of learning image data stored in alearning image database of Embodiment 1;

[0050]FIG. 6 illustrates an example of a combination of an input windowand a learning window released from similar window extracting means ofEmbodiment 1;

[0051]FIG. 7 illustrates an example of a resultant output of countingmeans;

[0052]FIG. 8 is a block diagram of an image recognizing apparatusaccording to Embodiment 2 of the present invention;

[0053]FIG. 9 is a flowchart showing a procedure in Embodiment 2;

[0054]FIG. 10 illustrates examples of same-type images stored in animage database of Embodiment 2;

[0055]FIG. 11 illustrates examples of same-type window data stored in asame-type window database of Embodiment 2;

[0056]FIG. 12 illustrates an example of a combination of an input windowand a learning window released from similar window extracting means ofEmbodiment 2;

[0057]FIG. 13 illustrates an example of a resultant output of countingmeans of Embodiment 2;

[0058]FIG. 14 is a block diagram of an image recognizing apparatusaccording to Embodiment 3 of the present invention;

[0059]FIG. 15 is a flowchart showing a procedure in Embodiment 3;

[0060]FIG. 16 illustrates examples of learning image data stored in alearning image database for type X of Embodiment 3;

[0061]FIG. 17 is a block diagram of an image recognizing apparatusaccording to Embodiment 4 of the present invention;

[0062]FIG. 18 is a block diagram of the image recognizing apparatus ofEmbodiment 4 implemented by a computer;

[0063]FIG. 19 is a flowchart showing a procedure of the operation ofmodel generating means of Embodiment 4;

[0064]FIG. 20 is a flowchart showing a procedure at an image input unitthrough an output unit in Embodiment 4;

[0065]FIG. 21 illustrates examples of model images and their shapeidentifiers stored in an image database of Embodiment 4;

[0066]FIG. 22 illustrates an example of an average model image and itsshape identifier stored in a shape database of Embodiment 4;

[0067]FIG. 23 illustrates examples of rectangular segments cut out by animage cutout unit;

[0068]FIG. 24 illustrates an example of a detection result released byan output unit;

[0069]FIG. 25 is a block diagram of an image recognizing apparatusaccording to Embodiment 5 of the present invention;

[0070]FIG. 26 is a flowchart showing a procedure of the operation atmodel generating means of Embodiment 5;

[0071]FIG. 27 is a flowchart showing a procedure of an operation at animage input unit through an output unit in Embodiment 5;

[0072]FIG. 28 illustrates examples of model images and their shapeidentifiers stored in an image database of Embodiment 5;

[0073]FIG. 29 is a block diagram of an image recognizing apparatusaccording to Embodiment 6 of the present invention;

[0074]FIG. 30 is a flowchart showing a procedure of an operation atmodel generating means of Embodiment 6;

[0075]FIG. 31 is a flowchart showing a procedure of an operation at animage input unit through an output unit in Embodiment 6;

[0076]FIG. 32 illustrates an example of model images and its shapeidentifier stored in an image database of Embodiment 6;

[0077]FIG. 33 illustrates examples of rectangular segments cut out by animage cutout unit of Embodiment 6;

[0078]FIG. 34 illustrates an example of a resultant output of a countingunit of Embodiment 6;

[0079]FIG. 35 is a block diagram showing a conventional imagerecognizing apparatus; and

[0080]FIG. 36 is a block diagram showing another conventional imagerecognizing apparatus.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0081] Exemplary embodiments of the present invention will be describedin detail referring to FIGS. 1 to 34.

[0082] (Embodiment 1)

[0083]FIG. 1 is a block diagram of an image recognizing apparatusaccording to Embodiment 1 of the present invention. Image input means 1receives image data of an object to be identified. Image dividing means2 divides the image received by image input means 1 into input windowsas local-segments. Similar window extracting means 3 retrieves, from adatabase, the data of a learning window which is similar to the localwindow from image dividing means 2, and releases the learning windowwith its corresponding local window as the learning-local-segments.Learning means 4 preliminarily generates an image model of the object tobe identified. Learning image database 41 divides a learning image,which is a model image of an object to be identified, into windowshaving the size as the local window from image dividing means 2 andstores them as learning windows. Object position estimating means 5calculates the position of the object in the input image from theposition of the learning window in the learning image retrieved bysimilar window extracting means 3 and the position of the correspondinglocal window in the input image. Counting means 6 counts a pair of thelocal window, which is received from object position estimating means 5,and the learning window for each position which is estimated from thewindow and the learning window. Object determining means 7 judgeswhether the object is present or not in the input image and, when so,determines the position of the object in the input image.

[0084]FIG. 2 is a block diagram showing an image recognizing apparatusimplemented by a computer. The image recognizing apparatus comprisescomputer 201, CPU 202, memory 203, keyboard and display 204, storagemedium unit 205 such as an FD, a PD, or an MO drive for holding an imagerecognizing program, interface (I/F) units 206, 207, and 208, CPU bus209, camera 210 for capturing an image, image database 211 for supplyingpre-stored image data, learning image database 212 for dividing thelearning image, which is the model image of each object of interest,into a window-sized segment and storing them as learning windows, andoutput terminal 213 for delivering the type and position of the objectvia the I/F units.

[0085] The operation of the image recognizing apparatus having the abovearrangement is now explained referring to the flowchart shown in FIG. 3.FIG. 4 illustrates an example of the input image, FIG. 5 illustratesexamples of the learning image, FIG. 6 illustrates an example of thedata output of similar window extracting means 3, and FIG. 7 illustratesan example of the resultant output of counting means 6.

[0086] In learning image database 41 (learning image database 212), thesame window-size of images of an object to be identified as the inputwindow, shown in FIG. 5, are stored as image data of a learning windowtogether with the coordinate at the center thereof. Learning images 1and 2 shown in FIG. 5 are provided for identifying a sedan-type vehiclein the shown direction and size.

[0087] Image input means 1, which is camera 210 or image database 211,receives an image data of interest (Step 301). Image dividing means 2retrieves an input window data of a predetermined size from the receivedimage through moving and locating a local window and releases the inputlocal window data with the coordinate at the center thereof (Step 302).

[0088] Similar window extracting means 3 calculates a difference betweenthe input local window data in the input image received from imagedividing means 2 and the corresponding learning window data stored inlearning image database 41 (learning image database 212) (e.g. a sum ofsquares of a pixel data difference or an accumulation of the absolutesof a pixel data difference) and picks up one of the learning window datawith the minimum difference. Picking up the most similar learning windowto every input local window in the input image from learning imagedatabase 41, similar window extracting means 3 releases the coordinatesat the center of the learning window and the coordinates at the centerof the corresponding input local window in a combination as shown inFIG. 6 (Step 303).

[0089] Object position estimating means 5, upon receiving a pair of thecoordinate data of the learning window and the coordinate data of theinput local window (Step 304), estimates the position of the object inthe input image (more specifically, the coordinates at the upper leftcorner of a rectangular which circumscribes the object, i.e., at theorigin of the learning image shown in FIG. 5) (Step 305). Input thecoordinates (α, β) of the input local window shown in FIG. 6 and thecoordinates (γ, θ) of the learning window, object position estimatingmeans releases the position of the object is expressed as (α-γ, β-θ).

[0090] Counting means 6, when receiving the coordinates (α-γ, β-θ)calculated at Step 305, increments the score for the coordinates by one(Step 306). As a procedure from Step 304 to Step 306 has been repeatedfor all the pairs of the input local window and the learning window(Step 307), counting means 6 releases a sum data including thecoordinates at the position and the score as shown in FIG. 7.

[0091] Object image determining means 7 then judges whether or not thescore for each set of the coordinates is greater than certain value T(Step 309). When so, it is judged that the object to be identified ispresent in the input image (Step 310). If none of the scores is greaterthan certain value T, it is determined that the object to be identifiedis not present in the input image (Step 311). The coordinates at theposition of the object are then passed through I/F unit 208 and releasedfrom output terminal 213 (Step 312).

[0092] (Embodiment 2)

[0093]FIG. 8 is a block diagram of an image recognizing apparatusaccording to Embodiment 2 of the present invention. Image input means801 receives image data of an object to be identified. image dividingmeans 802 divides the image data supplied from the image input means 801into an input window as local-segments and releases the input windowdata. Similar window extracting means 803 retrieves learning window(learning-local-segment) data, which is similar to the input localwindow data divided by image dividing means 802, from a database andreleases it together with the corresponding input local window data.Learning means 804 preliminarily generates a model of the object to beidentified. Learning image database 841 divides a learning image, whichrepresents the model of the object to be identified, into learningwindows having the same size as the input windows generated by imagedividing means 802 and stores the learning windows. Similar windowintegrating unit 842 makes a group of the learning windows which arestored in learning image database 841 and similar to each other andreleases the image data of a representative learning window of the grouptogether with the coordinates of each of the other learning windows inthe group. Integrating unit 842 also releases the coordinates and theimage data of a learning window which is dissimilar to the otherlearning windows. Same-type window database 843 stores the coordinatesand the image data of the representative learning window of each groupreceived from similar window integrating unit 842. Object positionestimating means 805 calculates the position of the object in the inputimage from the position of the learning window in the learning imageretrieved by similar window extracting means 803 and the position of itscorresponding input local window in the input image. Counting means 806counts a pair of the input local window and the learning window for eachposition which is estimated from the input window and the learningwindow by object position estimating means, when receiving a result ofthe counting operation of counting means 806, judges whether or not theobject is present in the input image and, if so, determines the positionof the object.

[0094] The operation of the image recognizing apparatus having the abovearrangement is now explained referring to the flowchart shown in FIG. 9.

[0095] While the input image is shown in FIG. 4 and the learning imagesare shown in FIG. 5, FIG. 10 illustrates similar windows stored insimilar window database 841, FIG. 11 illustrates same-type window datastored in same-type window database 843, FIG. 12 illustrates a dataoutput of similar window extracting means 803, and FIG. 13 illustrates aresultant output of counting means 806.

[0096] The image of each object to be identified is divided intolearning windows having the same size as the input local windows of theinput image shown in FIG. 5, and each learning window data is storedtogether with its window number and the coordinates at the centerthereof in learning image database 841. FIG. 5 shows such two learningwindows as learning images 1 and 2 for identifying a sedan-type vehiclein the shown direction and size. Same-type window database 843 storesthe image data of a representative learning window of each group of thesimilar windows, such as shown in FIG. 10, and the coordinates of eachof the other learning windows in the group, which are retrieved fromlearning image database 841 by similar window integrating unit 842 asshown in FIG. 11.

[0097] Image input means 801 receives image data of interest (Step 901).Image dividing means 802 extracts local windows of a predetermined sizefrom the image data as an input windows and releases their data togetherwith the coordinates at the center thereof (Step 902).

[0098] Similar window extracting means 803 calculates a differencebetween the input local window received from image dividing means 802and the representative learning window of each group stored in same-typewindow database 843 (e.g. a sum of the squares of a pixel datadifference or an accumulation of the absolutes of a pixel datadifference) and picks up a group having the minimum difference from thegroups. As picking up a group of the learning windows which are mostsimilar to the corresponding input local windows, similar windowextracting means 803 recognizes that all the learning windows in thegroup are similar (or corresponding) to the input local window.Extracting means 803 retrieves the coordinates of the representativelearning local window from same-type window database 843 and releasesthem together with the coordinates at the center of the input window andthose at the center of the learning window and the type of a vehicleattributed to the learning window as shown in FIG. 12 (Step 903).

[0099] Object position estimating means 805, upon receiving a pair ofthe coordinate data of the learning window and the coordinate data ofthe input local window (Step 904), estimates the position of the objectin the input image, and more specifically, the coordinates at the upperleft corner of a rectangular which circumscribes the object (i.e., theorigin in the learning image shown in FIG. 5), and releases its datatogether with the type of a vehicle (Step 905). Upon Input thecoordinates of the input local window (α, β) and the coordinates of thelearning window (γ, θ) as shown in FIG. 12, object position estimatingmeans 805 releases the position of the object (α-γ, β-θ).

[0100] Counting means 806, when receiving the coordinates (α-γ, β-θ)calculated at Step 905 together with data of the type of a vehicle,increments both the score for the coordinates and the score for the typeof a vehicle by one (Step 906).

[0101] It is then examined whether or not the procedure from Step 904 toStep 906 is completed for all the pairs of the input local window andthe learning window (Step 907), and when so, counting means 806 deliversthe coordinates of the position, the score for it, and the score foreach type of a vehicle in a combination, shown in FIG. 13, to objectimage determining means 807.

[0102] Object image determining means 807 then determines whether or notthe score for each set of the coordinates is greater than certain valueT (Step 909). When so, the coordinates at each position of which scoreis greater than T and the type of a vehicle of which score is higherthan any other scores is released (Step 910). If none of the scores isgreater than certain value T, determining means 807 determines theobject to be identified is not present in the input image (Step 911).The coordinates at the position and the type of the vehicle of theobject are then released from the output terminal 213 through I/F unit208 (Step 912).

[0103] (Embodiment 3)

[0104]FIG. 14 is a block diagram of an image recognizing apparatusaccording to Embodiment 3 of the present invention. Image input means1401 receives image data of an object to be identified. Image dividingmeans 1402 divides the image data supplied from image input means 1401into input windows as local-segments and releases the input windows.Similar window extracting means 1403 retrieves one similar learningwindow (learning local-segment) to each input local window released byimage dividing means 1402 from each learning database and releases ittogether with the corresponding input local window data. Learning means1404 preliminarily generates a model of the object which corresponds todifferent categories to be identified. By-character learning imagedatabases 1441, 1442, . . . divide a learning image which represents themodel of the object to be identified into learning windows having thesame size as the input windows determined by image dividing means 1402,and store the learning windows for each character. Object positionestimating means 1405 calculates the position of the object in the inputimage from the position of the learning window in the learning imageretrieved by similar window extracting means 1403 for each character andthe position of its corresponding input local window in the input image.Counting means 1406 counts a pair of the input local window and thelearning window for each position which is estimated from the inputwindow and the learning window by object position estimating means 1405for each character. Object determining means 1407, when receivingresults of the counting operation of counting means 1406 for eachcharacter, judges whether or not the object is present in the inputimage, and if so, determines the position of the object.

[0105] The operation of the image recognizing apparatus having the abovearrangement is now explained referring to the flowchart shown in FIG.15.

[0106] An input image is shown in FIG. 4, a learning image of character1 is shown in FIG. 5, an example of output data of similar windowextracting means 1403 is shown in FIG. 6, and the learning image ofcharacter 2 is shown in FIG. 16.

[0107] In each of by-character learning image databases 1441, 1442, . .. in learning means 1404, the image of the object to be identified ofeach character is divided into learning windows having the same size asthe input windows of the input image shown in FIG. 5, and the learningwindows are stored together with the window numbers and the coordinatesat the center thereof. FIG. 5 shows such learning windows stored incharacter-1 learning image database 1441. Two learning images 1 and 2are for identifying sedan-type vehicles in the shown direction and size.The learning images shown in FIG. 16 are stored in character 2 learningimage database 1442 for identifying buses in the same location anddirection as shown in FIG. 5.

[0108] Image input means 1401 receives image data of interest (Step1501). Image dividing means 1402 extracts a input windows from the imagedata through moving and locating a window of predetermined size andreleases the input window together with the coordinates at the centerthereof (Step 1502).

[0109] Similar window extracting means 1403 calculates a differencebetween the input local window of the input image received from imagedividing means 1402 and its corresponding learning window stored in eachby-character learning image database in learning means 1404 (e.g. a sumof the squares of a pixel data difference or an accumulation of theabsolutes of a pixel data difference), and picks up the learning windowhaving the minimum difference in each learning image database. Similarwindow extracting means 1403 picks up the most similar learning windowfor each input window from learning means 1404. Extracting means 1403retrieves and releases the coordinates at the center of the learningwindow together with the coordinates at the center of the correspondinginput window for each character as shown in FIG. 6 (Step 1503).

[0110] Object position estimating means 1405, upon receiving a pair ofthe coordinate data of the learning window and the coordinate data ofthe input local window (Step 1504), estimates the position of the objectin the input image, e.g., the coordinates at the upper left corner ofthe rectangular which circumscribes the object (i.e., the origin in thelearning image shown in FIG. 5) (Step 1505). Upon input the coordinates(α, β) of the input local window and the coordinates (γ, θ) of thelearning window as shown in FIG. 6, object position estimating means1405 releases the position (α-γ, β-θ) of the object.

[0111] Counting means 1406, when receiving the coordinates (α-γ, β-θ)calculated at Step 1505, increments the score for the coordinates of thewindow by one for each character (Step 1506).

[0112] It is then examined whether or not the procedure from Step 1504to Step 1506 is completed for all the pairs of the input local windowand the learning window for one character (Step 1507). The sameprocedure from Step 1504 to Step 1506 is repeated for another character.When the procedure has been completed for all the learning windows andthe input local windows for all characters, counting means 1406 deliversthe coordinates of the position and the score in a combination for eachcharacter shown in FIG. 7 to object image determining means 1407 (Step1508).

[0113] Object image determining means 1407 then determines whether ornot the score for each set of the coordinates is greater than certainvalue T (Step 1509). When object image determining means 1407 determinesthat the object of a particular character of which score is greater thanT and higher than any other scores is present in the input image, thecoordinates at the position of the object are released together withdata of the character (Step 1510). If none of the scores is greater thancertain value T, determining means 1407 determines that the object to beidentified is not present in the input image (Step 1511). Thecoordinates at the position and the type of the object are released fromoutput terminal 213 through I/F unit 208 (Step 1512).

[0114] Embodiment 4)

[0115]FIG. 17 is a block diagram of an image recognizing apparatusaccording to Embodiment 4 of the present invention.

[0116] Image database 1701 stores gradation images of objects having acommon shape to be identified, and each gradation image is accompaniedwith a shape identifier including a shape name, a file name, and thecoordinates at the upper left and the lower right corners of arectangular which circumscribes the object in an image. Model generatingmeans 1702 retrieves all the gradation images of each shape to beidentified from image database 1701 and extracts its characteristic.Characteristic level extracting unit 1721 calculates an average and avariance of each pixel in the rectangular which circumscribes the objectof each shape in all the gradation images received from image database1701 and releases them together with the corresponding shape identifier.Shape database 1703 receives and stores each set of the average, thevariance, and the shape identifier of each shape from characteristiclevel extracting unit 1721. Image input unit 1704 inputs an image to bedetermined whether the object having a shape to be identified is presenttherein. Image cutout unit 1705 receives the shape identifier from shapedatabase 1703, and cuts out an image segment having the same size as theshape to be identified from the input image. Shape classifying means1706 examines whether or not an object of the shape to be identified ispresent in the image segment received from image cutout unit 1705.Segment shape classifying unit 1761 compares the image segment receivedfrom image cutout unit 1705 with a shape characteristic retrieved fromshape database 1703 for determining that a shape in the image segmentcoincides with the shape characteristic. Output unit 1707, whenreceiving an output of the shape classifying means 1706 indicating thatthe object of the shape to be identified is present in the imagesegment, directs a display to display the shape and the position of theobject in the input image.

[0117]FIG. 18 is a block diagram of an image recognizing apparatusimplemented by a computer. The image recognizing apparatus comprisescomputer 1801, CPU 1802, memory 1803, keyboard and display 1804, storagemedium unit 1805 such as an FD, a PD, an MO, a DVD or the like forstoring an image recognizing program, interface (I/F) units 1806, 1807,and 1808, CPU bus 1809, camera 1810 for capturing an image, imagedatabase 1811 for supplying pre-stored image data, shape database 1812storing model images of objects of various shapes together with theircorresponding shape identifiers, and output terminal 1813 for deliveringthe shape and the position of the object identified via the I/F units.

[0118] The operation of the image recognizing apparatus having the abovearrangement is now explained referring to the flowcharts shown in FIGS.19 and 20. FIG. 19 is the flowchart showing an operation of the modelgenerating means. FIG. 20 is the flowchart showing a procedure frominputting an image data to be examined to outputting a result ofrecognition. FIG. 21 illustrates examples of the model image stored inimage database 1701. FIG. 22 illustrates an example of the average dataof one shape with its shape identifier stored in shape database 1703.FIG. 23 illustrates an input image received from image input unit 1704and includes rectangular image segments cut out by image cutout unit1705. FIG. 24 illustrates a resultant output of shape classifying means1706 displayed on the display with output unit 1707.

[0119] Prior to recognition, data about the shapes to be identified areprepared. Image database 1701 stores gradation images of various objectssuch as shown in FIG. 21 in the form of files as a model image. Eachimage is accompanied with a shape identifier including the shape image,an image file name, and the coordinates at the upper left and the lowerright corners of a rectangular which circumscribes the object as anobject area. The model images shown in FIG. 21 illustrate differentsedan-type vehicles captured from the common angle and distance by acamera.

[0120] When a “sedan A”-type vehicle such as shown in FIG. 21 is anobject of a shape to be identified, model generating means 1702retrieves all model images accompanied with a shape identifier includingthe shape name of “sedan A” from image database 1701 together with theshape identifier. Then, characteristic level extracting unit 1721calculates an average image of rectangular sized images determined asobjective areas (Step 1901). As the model images carry objects of thesame shape, their cutout image segments as the objective areas are equalin the size and the average image is also identical in the size.

[0121] The average image of “sedan A” shown in FIG. 21 consists of 148pixels in horizontal by 88 pixel in vertical. Then, characteristic levelextracting unit 1721 calculates a variance from the pixel in therectangular as the objective area and the corresponding pixel of theaverage image for each model image (Step 1902).

[0122] Finally, characteristic level extracting unit 1721 releases theaverage image of “sedan A”, the variance for each pixel, and thecorresponding shape identifier, then, shape database 1703 stores them(Step 1903). In case that a plurality of objects of shapes to beidentified are provided, the procedure from Step 1901 to Step 1903 isrepeated for examining the respective shapes.

[0123] For recognition of the “sedan A”-type vehicle, image input unit1704 (camera 210 or image database 211) supplies an input image (Step2001). Image cutout unit 1705 cuts out, from the input image, each imagesegment which is equal in the size to the average image of “sedan A”stored in shape database 1703 through moving the rectangular windowhaving the same size as the average image as shown in FIG. 23 (Step2002).

[0124] Shape classifying means 1706 receives one image segment fromimage cutout unit 1705 and the average image of “sedan A” and thevariance from shape database 1703. Segment shape classifying unit 1761calculates the square of a difference between each pixel of the imagesegment and the corresponding pixel of the average image, divides thesquare by the variance, and calculates a sum of the quotients todetermine the distance between the image segment and the average image(Step 2003). In case that the objects of shapes to be identified are twoor more, segment shape classifying unit 1761 repeats the operation ofStep 2003 for each shape (Step 2004). When the least calculateddistances is less than a certain value (Step 2005), it is judged thatthe image segment contains an object of the shape of the average imagewhich is pertinent to the least distance (Step 2006).

[0125] Segment shape classifying unit 1761 judges that no object ispresent in the image segment when the least distance is not less thanthe certain value (Step 2007). The above operation is repeated bysegment shape classifying unit 1761 for each segment image separatedfrom the input image (Step 2008). When it is judged that the imagesegment contains the object, output unit 1707 places the shape of theobject over the segment image in the input image as shown in FIG. 24(Step 2009). A resultant image is then released via I/F unit 1808 fromoutput terminal 813.

[0126] (Embodiment 5)

[0127]FIG. 25 is a block diagram of an image recognizing apparatusaccording to Embodiment 5 of the present invention.

[0128] Image database 2501 stores gradation images of various objects ofshapes to be identified. Database 2501 also stores a shape identifierspecifying the name of each shape, the image file name, and thecoordinates at the upper left and the lower right corners of arectangular of a predetermined size which circumscribes the object ofthe shape to be identified. Model generating means 2502 retrieves allthe gradation images of each shape of the object to be identified fromimage database 2501 and extracts the characteristic of the images.Characteristic space generating unit 2520 generates a characteristicspace from the model images received from image database 2501 andtransfers its base vector to shape database 2503 where each model imageis projected to the characteristic space as a model image vector.Characteristic level extracting unit 2521 calculates an average and avariance of all the model image vectors of the shapes received fromcharacteristic space generating unit 2502 for each shape and releasesthem together with the relevant shape identifier. Shape database 2503receives and stores the base vector in the characteristic space fromcharacteristic space generating unit 2520 and the average and varianceof the model image vectors of each shape with the shape identifier fromcharacteristic level extracting unit 2521. Image input unit 2504supplies an image to be determined whether the object of a shape to beidentified is present therein. Image cutout unit 2505 is responsive tothe shape identifier from shape database 2503 for cutting out a segmentimage, which is equal in the size to the shape to be identified, fromthe input image. Shape classifying means 2506 examines whether or not anobject of the shape to be identified is present in the image segmentreceived from image cutout unit 2505. Characteristic space projectingunit 2560 projects, to the characteristic space, the image segmentreceived from image cutout unit 2505 as an image segment vector based onthe base vector received from shape database 2503. Segment shapeclassifying unit 2561 calculates a distance between the segment imagevector received from characteristic space projecting unit 2560 and theaverage of model shape vectors retrieved from shape database 2503, andclassifying unit 2561 determines whether or not the image segmentcoincides the shape to be identified. Output unit 2507, when receivingan output of the shape classifying means 2506 indicating that the objectof the shape to be identified is present in the image segment, displaythe shape and the position of the object in the image segment on adisplay.

[0129] The operation of the image recognizing apparatus having the abovearrangement is now explained referring to the flowcharts shown in FIGS.26 and 27. FIG. 26 is the flowchart showing an operation of the modelgenerating means. FIG. 27 is the flowchart showing a procedure frominputting an image to be examined through outputting a result ofrecognition. FIG. 28 illustrates examples of the model image stored inimage database 2501. FIG. 23 illustrates an example of a rectangularsegment image which is cut out by image cutout unit 2505 from an inputimage received from image input unit 2504. FIG. 24 illustrates aresultant output of shape classifying means 2506 displayed on thedisplay.

[0130] Prior to recognition, databases about the shapes to be identifiedare prepared. Image database 2501 stores gradation images of variousobjects in the form of files such as model images as shown in FIG. 28.Each image is accompanied with the shape identifier specifying a shapeof an object, an image file name, and the coordinates at the upper leftand the lower right corners of a rectangular which circumscribes theobject as an image area. The model images shown in FIG. 28 show asedan-type vehicle and a bus captured from the common angle and distanceby a camera.

[0131] When the “sedan A”-type vehicle and the bus shown in FIG. 28 arethe objects of the shapes to be identified, model generating means 2502retrieves all the model images of vehicles accompanied with the shapeidentifier specifying the shape name of “sedan A” and all the modelimages accompanied with the shape identifier specifying the shape nameof “bus rear portion” from image database 2501 and transfers them tocharacteristic space generating unit 2520 together with the shapeidentifiers.

[0132] Characteristic space generating unit 2520 calculates aneigenvalue and an eigenvector from the pixel in the rectangular area inthe model image (Step 2601). The rectangular areas in each model imageare equal in the size, each consisting of 148 pixels in horizontal by 88pixels in vertical as shown in FIG. 28. For the bus, the rectangularshape shown in FIG. 28 is an objective area at the same position for allthe model images of buses. A vector formed as a row of all the pixels ineach model image is generated, and an average vector of the vectors iscalculated and subtracted from each vector for determining theeigenvalue and an eigenspace.

[0133] Characteristic space generating unit 2520 stores the eigenvectorscorresponding to the N greatest eigenvalues as base vector in shapedatabase 2503 (Step 2602). Using the N eigenvalues, generating unit 2520projects each model image as a model image vector in the characteristicspace (Step 2603).

[0134] Characteristic level extracting unit 2521 receives the modelimage vector with its shape identifier from characteristic spacegenerating unit 2520 and calculates an average and a covariance of themodel image vectors having the same shape identifier (Step 2604).Characteristic level extracting unit 2521 releases the average of themodel images and the average and the covariance of model image vectorsof each shape together with their corresponding shape identifiers andshape database 2503 stores them (Step 2605).

[0135] For the recognition, an image to be identified is supplied fromimage input unit 2504 (Step 2701). Image cutout unit 2505 determines thesize of a mode image from the objective area specified by the shapeidentifier stored in shape database 2503. Then, image cutout unit 2505cuts out image segments having the same size through moving a windowfrom the input image as shown in FIG. 23 (Step 2702).

[0136] Shape classifying means 2506 receives one image segment fromimage cutout unit 2505 and the base vector from shape database 2503.Characteristic space projecting unit 1760 projects the image segment asa image segment vector in the eigenspace (Step 2703). Segment shapeclassifying unit 2561 receives the image segment vector fromcharacteristic space projecting unit 2560 and the average vectors andcovariances of the “sedan A”-type vehicle and the bus from shapedatabase 2503 respectively, and calculates a Mahalanobis distancebetween the image segment vector and the average vector (Step 2704).

[0137] When the least Mahalanobis distances is less than a certain value(Step 2705), it is judged that the image segment contains an object ofthe shape pertinent to the average vector of the least distance (Step2706). If the least distance is not less than the certain value, it isjudged that the image segment contains no object to be identified (Step2707). characteristic space projecting unit 2560 and segment shapeclassifying unit 2561 repeats the process from Step 2703 to Step 2707for each of the image segments which are cut out from the input image(Step 2708). When it is judged that the image segment contains theobject, output unit 2507 places the shape of the object over the imagesegment in the input image as shown in FIG. 24 (Step 2709). A resultantimage is then released via I/F unit 1808 from output terminal 1813.

[0138] (Embodiment 6)

[0139]FIG. 29 is a block diagram of an image recognizing apparatusaccording to Embodiment 6 of the present invention.

[0140] Image database 2901 divides each of gradation images of variousobjects of the shape to be identified into rectangular shape segmentshaving a predetermined size and stores each of the shape segments withthe shape identifier specifying a shape name, a file name, andcoordinates at the upper left and the lower right corners of the shapesegment. Model generating means 2902 retrieves all the gradation imagesof the object of the shape to be identified from image database 2901 andextracts its characteristic. Characteristic space generating unit 2920generates a characteristic space from the pixel values of all the shapesegments in each model image received from image database 2901 andtransfers its base vector to shape database 2903 where each shapesegment is projected as a model image local vector to the characteristicspace. Characteristic level extracting unit 2921 calculates an averageand variance of all the model image local vectors received fromcharacteristic space generating unit 2902 for each shape segment andreleases them together with the relevant shape identifier. Shapedatabase 2903 receives the base vector of the characteristic space fromcharacteristic space generating unit 2920 and the average and varianceof the model image local vectors together with the shape identifier foreach shape segment from characteristic level extracting unit 2921, andstores them. Image input unit 2904 supplies an input image to bedetermined whether an object of a shape to be identified is presenttherein. Image cutout unit 2905 is responsive to a shape identifier fromshape database 2903 and cuts out an image segment having the same sizeas the shape segment from the input image. Shape classifying means 2906examines whether or not an object of a shape to be identified is presentin the image segment received from image cutout unit 2905.Characteristic space projecting unit 2960 projects the image segmentreceived from image cutout unit 2905 to the characteristic space as animage segment vector based on the base vector received from shapedatabase 2903. Segment shape classifying unit 2961 calculates a distancebetween the image segment vector received from characteristic spaceprojecting unit 2960 and each average of model image local vectorsretrieved from shape database 2903 and determines whether or not theimage segment vector matches the shape segment of the shape of theobject to be identified. As shape segment classifying means 2961 detectsthe shape segment of the shape of the object to be identified, overallshape area estimating unit 2962 estimates the area in which the overallshape of the object exists in the input image from the position of theshape segment in relation to the overall shape. Counting unit 2963counts the position of the overall shape of the object received from theoverall shape area estimating unit 2962 for each image segmentcontaining the shape segment of the shape of the object. Upon judgingthat the object is located at the position which is determined a numberof times greater than a certain number by counting unit 2963, outputunit 2907 displays the shape and the position of the object on adisplay.

[0141] The operation of the image recognizing apparatus having the abovearrangement is now explained referring to the flowcharts shown in FIGS.30 and 31. FIG. 30 is the flowchart showing an operation of the modelgenerating means. FIG. 31 is the flowchart showing a procedure frominputting an image to be examined through outputting a result ofrecognition. FIG. 32 illustrates examples of the model image stored inimage database 2901. FIG. 33 illustrates an input image which isreceived from image input unit 2904 and includes rectangular imagesegments cut out by image cutout unit 2905. FIG. 34 illustrates aresultant output of counting unit 2963. The resultant output of shapeclassifying means 2906 to be displayed on the display is shown in FIG.24.

[0142] Prior to the recognition, data about the shapes to be identifiedare prepared. Image database 2901 stores gradation images of the objectin the form of files such as shown in FIG. 32. Each gradation image isdivided into local-segments having a predetermined size of a rectagularand each local-segment is accompanied with the shape identifierspecifying a local-segment name, a file name, and the coordinates at theupper left and the lower right corners of the local-segment. Thelocal-segment name comprises the name of an overall shape of “sedan A”of the object, and a number identifying the local-segment in the overallshape. The number denotes the same position regardless of the overallshape of the object to be identified. The local-segments may overlapeach other. FIG. 32 shows an example of a model image for identifying asedan-type vehicle captured by a camera from the shown angle anddistance. Actually, local-segments of plural images of similar lookingsedan-type vehicles are stored together with shape identifiers of thelocal-segments.

[0143] When the “sedan A”-type vehicle such as shown in FIG. 32 is anobject to be identified, model generating means 2902 retrieves all themodel images of vehicles accompanied with the shape identifier of “sedanA” from image database 2901 and transfers them with the shapeidentifiers to characteristic space generating unit 2920. Generatingunit 2920 calculates an eigenvalue and an eigenvector from the pixelvalue in each rectangular local-segment in the model image accompaniedwith the shape identifier as a local model image (Step 3001). The localmodel images in each model image are equal in the size, each consistingof 29 pixels in horizontal by 22 pixels in vertical as shown in FIG. 32.To calculate the eigenvector, a vector which is a row of the pixels ineach local model image is generated, and an average of the vectors iscalculated and subtracted from each vector for determining theeigenvalue and an eigenspace.

[0144] Characteristic space generating unit 2920 generates theeigenvector corresponding to N greatest eigenvalues as the base vector,and shape database 2903 stores it (Step 3002). Using the N eigenvalues,characteristic space generating unit 2920 projects each local modelimage in the characteristic space to generate a local model image vector(Step 3003). Characteristic level extracting unit 2921 receives thelocal model image vector with its shape identifier from characteristicspace generating unit 2920 and calculates an average and covariance ofthe local model image vectors accompanied with the same shape identifier(Step 3004). Characteristic level extracting unit 2921 releases theaverage of all the local model images and the average and covariance oflocal model vectors for each shape, and shape database 2903 stores themtogether with their corresponding shape identifiers (Step 3005).

[0145] For recognition, an image to be determined whether an object tobe identified is present therein is supplied from image input unit 2904(Step 3101). Image cutout unit 2905 calculates the size of alocal-segment from the objective area determined by the shape identifierstored in shape database 2903. Then, image cutout unit 2905 cuts outeach segment having the same size from the input image through moving awindow as shown in FIG. 33 (Step 2702).

[0146] Shape classifying means 2906 receives one image segment fromimage cutout unit 2905 and the base vector from shape database 2903.Characteristic space projecting unit 2960 projects the image segment inthe characteristic space as a partial image vector (Step 3103). Segmentshape classifying unit 2961 receives the image segment vector fromcharacteristic space projecting unit 2960 and the average vectors andcovariances of local-segments of “sedan A” from shape database 2903 tocalculate a Mahalanobis distance between the image segment vector andeach average vectors (Step 3104). Then, classifying unit 2961 releasesthe shape identifier belonging to the local-segment having the averagevector pertinent to the least distance.

[0147] Overall shape estimating unit 2962 calculates a differencebetween the coordinates at the upper left corner of the objective areadefined by the shape identifier and the coordinates at the upper leftcorner of the image segment in the input image, and counting unit 2961increments the score for the coordinates by one (Step 3105). Thecoordinates for which score is incremented represent the position of theobject in the input image.

[0148] Shape classifying means 2906 including characteristic spaceprojecting unit 2960 through counting unit 2963 repeats the process fromStep 3103 to Step 3105 for all the image segments which are cut out fromthe input image (Step 3106). The result in counting unit 2963 is shownin FIG. 34. In FIG. 34, a series of the coordinates are listed with thescore for them in the order from the highest score. When the score forthe coordinates is greater than a certain number (Step 3107), it isjudged that the object is present at the coordinates in the input image(Step 3108). Then, output unit 2907 places the shape of the object overthe image segment in the input image as shown in FIG. 24 (Step 3110). Ifnone of the scores is greater than the certain number at Step 3107, itis then judged that the input image carries no object to be identified(Step 3109), and the input image is directly released (Step 3110). Aresultant image is then released via I/F unit 1808 from output terminal1813.

[0149] As set forth above, the object recognizing apparatuses of thepresent invention can readily detect a characteristic of a shape ofobjects from a less amount of model data even if the surface color ofthe object is different. Also, even if an object to be identified ispartially visible in an input image, the apparatuses of the presentinvention can detect its shape and position can be detected at higheraccuracy.

What is claimed is:
 1. An image recognizing method comprising the stepsof: (a) dividing an input image into local-segments; (b) registering alearning image into a learning image database; (c) extracting alearning-local-segment which is similar to one of the local-segmentsfrom the learning image database; (d) relating the learning-localsegment extracted in the step (c) to the one of the local-segments; (e)estimating a position of an object to be identified in the input imagefrom coordinates of the one of the local-segments and coordinates of thelearning-local-segment; (f) counting a pair of one of the local-segmentsand learning-local-segment from which a first position is estimated todetermine a score for the first position; and (g) judging that theobject to be identified is present at the first position when the scoreis greater than a predetermined number.
 2. An image recognizing methodcomprising the steps of: (a) dividing an input image intolocal-segments; (b) dividing a learning image intolearning-local-segments having a same size as the local-segments andmaking a group of some of the learning-local-segments which are similarto each other; (c) registering image data of a representativeearning-local-segment of the group and coordinates of all the some ofthe learning-local-segments into a same-type window database; (d)extracting a representative learning-local-segment which is similar toone of the local-segments from the same-type window database; (e)relating the one of the local-segments to a group of which therepresentative learning-local-segment extracted in the step (d); (f)estimating a position of an object to be identified in the input imagefrom coordinates of the one of the local-segment and coordinates of therepresentative learning-local-segment of the group; (g) counting a pairof one of the local segments and a representative learning-local-segmentfrom which a first position is estimated to determine a score for thefirst position; and (h) judging that the object to be identified ispresent at the first position when the score is greater than apredetermined number.
 3. The image recognizing method according to claim1, wherein: said step (b) comprises the step of registering the learningimage into the learning image database by a character of an object to beidentified; said step (c) comprises the step of extracting thelearning-local-segment which is similar to the one of the local-segmentfrom the learning image database by the character; and said step (f)comprises the step of counting a pair of one of the local-segments and alearning-local-segment by the character.
 4. The image recognizing methodaccording to claim 2, wherein said step (c) comprises the step ofregistering image data of the representative learning-local-segment ofthe group and coordinates of all the some of the learning-local-segmentsin the group and a character of an object to be identified into thesame-type window database.
 5. The image recognizing method according toclaim 1, wherein the step (d) comprises the steps of: (d-1) calculatinga sum of one of (i) each square of a difference between a pixel value ofthe one of the local-segment and a pixel value of thelearning-local-segments and (ii) each absolute of the difference, andextracting a pair of one of the local-segments and alearning-local-segment which has minimum one of the sum; and (d-2)relating the one of the local-segment to the earning-local-segment inthe pair extracted in said step (d-1).
 6. The image recognizing methodaccording to claim 2, wherein said step (e) comprises the steps of:(e-1) calculating a sum of one of (i) each square of a differencebetween a pixel value of the one of the local-segment and a pixel valueof the representative learning-local-segment and (ii) each absolute ofthe difference, and extracting a pair of one of the local-segment and arepresentative learning-local-segment which has minimum one of the sum;and (e-2) relating the one of the local-segment to the representativelearning-local-segment in the pair extracted in said step (e-1).
 7. Animage recognizing apparatus comprising: image dividing means fordividing an input image into local-segments; learning means forregistering a learning image into a learning image database; similarwindow extracting means for extracting a learning-local-segment which issimilar to one of the local-segments from the learning image databaseand for relating the learning-local-segment to the one of thelocal-segment; object position estimating means for estimating aposition of an object to be identified in the input image fromcoordinates of the one of the local-segment and coordinates of thelearning-local-segment; counting means for counting a pair of one of thelocal-segments and a learning-local-segment from which a first positionis estimated by said object position estimating means to determine ascore for the first position to determine a score for the firstposition; and object determining means for judging that the object to beidentified is present in the first position when the score is greaterthan a predetermined number.
 8. An image recognizing apparatuscomprising: image dividing means for dividing an input image intolocal-segments; learning means for dividing a learning image intolearning-local-segments having a same size as the local-segments and formaking a group of some of the learning-local-segments which are similarto each other and for registering a representativelearning-local-segment of the group and coordinates of all the some ofthe learning-local segments into a same-type window database; similarwindow extracting means for extracting from the same-type windowdatabase the representative learning-local-segment of the group which issimilar to one of the local-segments of the input image and for relatingthe learning-local-segments to the one of the local-segment; objectposition estimating means for estimating a position of an object to beidentified in the input image from coordinates of the one of thelocal-segment and coordinates of the learning-local-segment; countingmeans for counting a pair of one of the local-segments and alearning-local-segments from which a first position is estimated by saidobject position estimating means to determine a score for the firstposition; and object determining means for judging that the object to beidentified is present at the first position when the score is greaterthan a predetermined number.
 9. An image recognizing apparatuscomprising: image dividing means for dividing an input image intolocal-segments; learning means for registering learning images by acharacter of a object to be identified into a learning image database;similar window extracting means for extracting a learning-local-segmentwhich is similar to one of the local-segments from the learning imagedatabase by the character and for relating the learning-local-segment tothe one of the local-segment by the character; object positionestimating means for estimating a position of an object to be identifiedfrom coordinates of the one of the local-segment and coordinates of thelearning-local-segment by the character; counting means for counting apair of one of the local-segments and a learning-local-segment fromwhich a first position is estimated by said object position estimatingmeans to determine a score for the first position by the character; andobject determining means for judging that the object to be identified ispresent at the first position when the score is greater than apredetermined number.
 10. The image recognizing apparatus according toclaim 8, wherein said learning means includes: similar windowintegrating means for making a group of some of thelearning-local-segments which are similar to each other stored in thelearning image database and for releasing image data of a representativelearning-local-segment of the group and coordinates of all the some ofthe learning-local-segments in the group; and a same-type windowdatabase for storing the image data of the representativelearning-local-segment of the group and the coordinates of all the someof the learning-local-segments in the group.
 11. A computer-readablestorage medium holding a program for making a computer carry out animage recognizing method, said image recognizing method comprising thesteps of: (a) dividing an input image into local-segments; (b)registering a learning image into a learning image database; (c)extracting a learning-local-segment which is similar to one of thelocal-segment of the input image from the learning image database; (d)relating the learning-local-segment extracted in the step (c) to the oneof the local-segments; (e) estimating a position of an object to beidentified in the input image from coordinates of the one of thelocal-segments and coordinates of the learning-local-segment; (f)counting a pair of one of the local-segments and alearning-local-segment from which a first position is estimated todetermine a score for the first position; and (g) judging that theobject to be identified is present at the first position when the scoreis greater than a predetermined number.
 12. An image recognizingapparatus for detecting a shape of an object from an image, comprising:an image database into which a shape identifier specifying the shape ofthe object and a model image, which is a image of the object having theshape, are preliminarily registered; model generating means forextracting characteristic data of the shape from the model image; ashape database for storing the characteristic of the shape with theshape identifier in a combination; an image input unit for supplying aninput image; an image cutout unit for cutting out an image segment fromthe input image; shape classifying means for comparing the image segmentwith the characteristic data of the shape to determine whether or notthe object of the shape is present in the image segment; and an outputunit for releasing data about the shape of the object determined by saidshape classifying means and data about a position of the shape of theobject in the input image.
 13. The image recognizing apparatus accordingto claim 12, wherein said model generating means is operative to:extract an average image of the model image of the shape and a varianceof each pixel in the model image as the characteristic data of theshape; and release a combination of the average image, the variance, andthe shape identifier into the shape database.
 14. An image recognizingapparatus for detecting a shape of an object from an image, comprising:an image database preliminarily storing a shape identifier specifyingthe shape of the object and a model image which is an image of theobject of the shape; model generating means for calculating a basevector in a characteristic space from a pixel value of the model image,for projecting the model image in the characteristic space as a modelimage vector, for calculating a characteristic statistic value of theshape from the model image vector having the shape identifier as acharacteristic shape parameter, and for adding the shape identifier tothe characteristic shape parameter; a shape database for storing thebase vector, the characteristic shape parameter, and the shapeidentifier in a combination; an image input unit for supplying an inputimage; an image cutout unit for cutting out an image segment from theinput image; shape classifying means for projecting the image segment inthe characteristic space to determine an image segment vector based onthe base vector and for comparing the image segment vector with themodel image using the characteristic shape parameter to determinewhether or not the shape of the object is present in the image segment;and an output unit for releasing data about the shape of the object anddata about a position of the shape of the object in the input image whenan object of which shape coincides the shape to be detected is presentin the input image.
 15. The image recognizing apparatus according toclaim 14, wherein said model generating means is operative to calculatethe characteristic shape parameter from an average vector and acovariance of the model image vector derived from the model image. 16.The image recognizing apparatus according to claim 14, wherein saidmodel generating means is operative to calculate an average image of themodel image, calculate a base vector from a pixel value of the averageimage, project the model image in the characteristic space as a modelimage vector, and add the shape identifier to the model image vector.17. The image recognizing apparatus according to claim 14, wherein theshape identifier includes data indicating what portion of the object theshape is.
 18. The image recognizing apparatus according to claim 17,wherein said shape classifying means is operative to estimate an overallarea which the object occupies in the input image from the image segmentof the shape identifier and sum up the overall area estimated for theimage segment to output a position of the overall area of the object.19. An image recognizing method for detecting a shape of an object froman image, comprising the steps of: registering a shape identifierspecifying the shape of the object to be identified and an image of theobject having the shape as a model image into an image database;extracting a characteristic data of the shape from the model image;releasing the characteristic data of the shape and the shape identifierin a combination into a shape database; supplying an input image to bedetermined whether or not the object is present therein; cutting out animage segment from the input image; comparing the image segment with thecharacteristic data of the shape to determine whether or not the objectof the shape to be identified is present in the image segment; andreleasing data about the shape of the object and data about a positionof the shape of the object in the input image.